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LiteFlowNet

LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation

Abstract

FlowNet2, the state-of-the-art convolutional neural network (CNN) for optical flow estimation, requires over 160M parameters to achieve accurate flow estimation. In this paper we present an alternative network that outperforms FlowNet2 on the challenging Sintel final pass and KITTI benchmarks, while being 30 times smaller in the model size and 1.36 times faster in the running speed. This is made possible by drilling down to architectural details that might have been missed in the current frameworks: (1) We present a more effective flow inference approach at each pyramid level through a lightweight cascaded network. It not only improves flow estimation accuracy through early correction, but also permits seamless incorporation of descriptor matching in our network. (2) We present a novel flow regularization layer to ameliorate the issue of outliers and vague flow boundaries by using a feature-driven local convolution. (3) Our network owns an effective structure for pyramidal feature extraction and embraces feature warping rather than image warping as practiced in FlowNet2. Our code and trained models are available at https://github.com/twhui/LiteFlowNet.

Results and Models

Models Training datasets FlyingChairs Sintel (training) KITTI2012 (training) KITTI2015 (training) Log Config Download
clean final EPE Fl-all EPE
LiteFlowNet-pre-M6S6 Flying Chairs 4.43 - - - - - log Config Model
LiteFlowNet-pre-M6S6R6 Flying Chairs 4.07 - - - - - log Config Model
LiteFlowNet-pre-M5S5R5 Flying Chairs 2.98 - - - - - log Config Model
LiteFlowNet-pre-M4S4R4 Flying Chairs 2.20 - - - - - log Config Model
LiteFlowNet-pre-M3S3R3 Flying Chairs 1.71 - - - - - log Config Model
LiteFlowNet-pre (LiteFlowNet-pre-M2S2R2) Flying Chairs 1.38 2.74 4.52 6.49 37.99% 15.41 log Config Model
LiteFlowNet Flying Chairs + Flying Thing3d subset - 2.47 4.30 5.42 32.86$ 13.50 log Config Model
LiteFlowNet-ft Flying Chairs + Flying Thing3d subset + Sintel - 1.47 2.06 - - - log Config Model
LiteFlowNet-ft Flying Chairs + Flying Thing3d subset + KITTI - 1.07 5.45% 1.45 log Config Model

Citation

@inproceedings{hui2018liteflownet,
  title={Liteflownet: A lightweight convolutional neural network for optical flow estimation},
  author={Hui, Tak-Wai and Tang, Xiaoou and Loy, Chen Change},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={8981--8989},
  year={2018}
}